7 papers
Do Large Language Models Perform Well on Comprehending Poetic Logic in Modern Chinese Poetry?
Tian Lan, Shanshan Wang, Zehua Duo +4
Large Language Models (LLMs) have achieved significant progress across a wide range of natural language processing (NLP) tasks, yet their ability to understand literary texts, part…
A Heuristic Perspective on Debiasing Language Models
Tian Lan, Yemin Wang, Chuancheng Shi +6
Language models (LMs) often acquire various biases during pre-training and may express them in interactions, potentially causing social harm. Existing methods often rely on counter…
Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language
Dianqing Lin, Tian Lan, Jiali Zhu +7
While large language models (LLMs) have achieved remarkable success in general language tasks, their performance on Chouxiang Language, a representative subcultural language in the…
Who Wrote This Line? Evaluating the Detection of LLM-Generated Classical Chinese Poetry
Jiang Li, Tian Lan, Shanshan Wang +5
The rapid development of large language models (LLMs) has extended text generation tasks into the literary domain. However, AI-generated literary creations has raised increasingly…
McBE: A Multi-task Chinese Bias Evaluation Benchmark for Large Language Models
Tian Lan, Xiangdong Su, Xu Liu +4
As large language models (LLMs) are increasingly applied to various NLP tasks, their inherent biases are gradually disclosed. Therefore, measuring biases in LLMs is crucial to miti…
RepCali: High Efficient Fine-tuning Via Representation Calibration in Latent Space for Pre-trained Language Models
Fujun Zhang, Xiaoying Fan, XiangDong Su +1
Fine-tuning pre-trained language models (PLMs) has become a dominant paradigm in applying PLMs to downstream tasks. However, with limited fine-tuning, PLMs still struggle with the…